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<span class="breadcrumb-node">Fielddata 的过滤</span>
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<h2 class="title">
<a id="_fielddata_filtering"></a>Fielddata 的过滤<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elasticsearch-cn/elasticsearch-definitive-guide/edit/cn/300_Aggregations/105_filtering.asciidoc">edit</a>
</h2>
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<p>设想我们正在运行一个网站允许用户收听他们喜欢的歌曲。
为了让他们可以更容易的管理自己的音乐库，用户可以为歌曲设置任何他们喜欢的标签，这样我们就会有很多歌曲被附上 <code class="literal">rock（摇滚）</code> 、 <code class="literal">hiphop（嘻哈）</code> 和 <code class="literal">electronica（电音）</code> ，但也会有些歌曲被附上 <code class="literal">my_16th_birthday_favorite_anthem</code> 这样的标签。</p>
<p>现在设想我们想要为用户展示每首歌曲最受欢迎的三个标签，很有可能 <code class="literal">rock</code> 这样的标签会排在三个中的最前面，而 <code class="literal">my_16th_birthday_favorite_anthem</code> 则不太可能得到评级。
尽管如此，为了计算最受欢迎的标签，我们必须强制将这些一次性使用的项加载到内存中。</p>
<p>感谢 fielddata 过滤，我们可以控制这种状况。我们 <em>知道</em> 自己只对最流行的项感兴趣，所以我们可以简单地避免加载那些不太有意思的长尾项：</p>
<div class="pre_wrapper lang-js">
<pre class="programlisting prettyprint lang-js">PUT /music/_mapping/song
{
  "properties": {
    "tag": {
      "type": "string",
      "fielddata": { <a id="CO220-1"></a><i class="conum" data-value="1"></i>
        "filter": {
          "frequency": { <a id="CO220-2"></a><i class="conum" data-value="2"></i>
            "min":              0.01, <a id="CO220-3"></a><i class="conum" data-value="3"></i>
            "min_segment_size": 500  <a id="CO220-4"></a><i class="conum" data-value="4"></i>
          }
        }
      }
    }
  }
}</pre>
</div>
<div class="calloutlist">
<table border="0" summary="Callout list">
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO220-1"><i class="conum" data-value="1"></i></a></p>
</td>
<td align="left" valign="top">
<p><code class="literal">fielddata</code> 关键字允许我们配置 fielddata 处理该字段的方式。</p>
</td>
</tr>
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO220-2"><i class="conum" data-value="2"></i></a></p>
</td>
<td align="left" valign="top">
<p><code class="literal">frequency</code> 过滤器允许我们基于项频率过滤加载 fielddata。</p>
</td>
</tr>
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO220-3"><i class="conum" data-value="3"></i></a></p>
</td>
<td align="left" valign="top">
<p>只加载那些至少在本段文档中出现 1% 的项。</p>
</td>
</tr>
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO220-4"><i class="conum" data-value="4"></i></a></p>
</td>
<td align="left" valign="top">
<p>忽略任何文档个数小于 500 的段。</p>
</td>
</tr>
</table>
</div>
<p>有了这个映射，只有那些至少在 <em>本段</em> 文档中出现超过 1% 的项才会被加载到内存中。我们也可以指定一个 <code class="literal">最大</code> 词频，它可以被用来排除 <em>常用</em> 项，比如 <a class="xref" href="stopwords.html" title="停用词: 性能与精度">停用词</a> 。</p>
<p>这种情况下，词频是按照段来计算的。这是实现的一个限制：fielddata 是按段来加载的，所以可见的词频只是该段内的频率。但是，这个限制也有些有趣的特性：它可以让受欢迎的新项迅速提升到顶部。</p>
<p>比如一个新风格的歌曲在一夜之间受大众欢迎，我们可能想要将这种新风格的歌曲标签包括在最受欢迎列表中，但如果我们倚赖对索引做完整的计算获取词频，我们就必须等到新标签变得像 <code class="literal">rock</code> 和 <code class="literal">electronica</code> ）一样流行。
由于频度过滤的实现方式，新加的标签会很快作为高频标签出现在新段内，也当然会迅速上升到顶部。</p>
<p><code class="literal">min_segment_size</code> 参数要求 Elasticsearch 忽略某个大小以下的段。 如果一个段内只有少量文档，它的词频会非常粗略没有任何意义。
小的分段会很快被合并到更大的分段中，某一刻超过这个限制，将会被纳入计算。</p>
<div class="tip admon">
<div class="icon"></div>
<div class="admon_content">
<p>通过频次来过滤项并不是唯一的选择，我们也可以使用正则式来决定只加载那些匹配的项。例如，我们可以用 <code class="literal">regex</code> 过滤器  处理 twitte 上的消息只将以 <code class="literal">#</code> 号开始的标签加载到内存中。
这假设我们使用的分析器会保留标点符号，像 <code class="literal">whitespace</code> 分析器。</p>
</div>
</div>
<p>Fielddata 过滤对内存使用有 <em>巨大的</em> 影响，权衡也是显而易见的：我们实际上是在忽略数据。但对于很多应用，这种权衡是合理的，因为这些数据根本就没有被使用到。内存的节省通常要比包括一个大量而无用的长尾项更为重要。</p>
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